Systems and methods for identifying and grouping related content labels

ABSTRACT

Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/379,436, filed on Apr. 9, 2019 and entitled “SYSTEMS AND METHODS FORIDENTIFYING AND GROUPING RELATED CONTENT LABELS”, which is acontinuation of U.S. patent application Ser. No. 14/829,522, filed onAug. 18, 2015 and entitled “SYSTEMS AND METHODS FOR IDENTIFYING ANDGROUPING RELATED CONTENT LABELS”, all of which are incorporated hereinby reference in their entireties.

FIELD OF THE INVENTION

The present technology relates to the field of providing content. Moreparticularly, the present technology relates to techniques foridentifying and grouping related content labels.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices, for example,to interact with one another, access content, share content, and createcontent. In some cases, users can utilize their computing devices tocapture, upload, or otherwise provide content. In one example, a user ofa social networking system (or service) can utilize his or her computingdevice to record, produce, and post content, such as text, links,images, video, or audio. In this example, the user can further provideone or more labels, such as hashtags, for the content.

Under conventional approaches to utilizing hashtags, content items canbe tagged or otherwise associated with various hashtags. Often times,under conventional approaches, such hashtags can be undesirablyrepetitive or redundant. For instance, the user may post a particularcontent item with a plurality of substantially similar or relatedhashtag labels. In this instance, another user who views or accesses theparticular content item will see the plurality of substantially similaror related hashtag labels, which can be unnecessarily repetitive orredundant for the other user. Furthermore, in some cases, conventionalapproaches can provide a list of trending or popular labels. However, inaccordance with conventional approaches, the list can include a numberof substantially similar or related labels, which can be uninterestingto users who view the list. As such, conventional approaches can createchallenges for or reduce the overall user experience associated withutilizing labels such as hashtags.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toacquire a set of labels associated with a set of content items. Eachlabel in the set of labels can be associated with at least one contentitem in the set of content items. It can be determined that at least twolabels, out of the set of labels, are related. The at least two labelscan be determined to be related based on at least one of a co-occurrencemetric associated with the at least two labels or a topic similaritymetric associated with the at least two labels. One label can beselected, out of the at least two labels, as being representative of theat least two labels.

In an embodiment, the at least two labels can be determined to berelated based on the co-occurrence metric. Determining that the at leasttwo labels are related can further comprise identifying each particularcontent item, out of the set of content items, that is associated withthe at least two labels. The co-occurrence metric associated with the atleast two labels can be incremented for each particular content itemthat is associated with the at least two labels. It can be determinedthat the co-occurrence metric at least meets a specified co-occurrencethreshold.

In an embodiment, the at least two labels can be determined to berelated based on the co-occurrence metric. Determining that the at leasttwo labels are related can further comprise determining a number oftimes in which a first label and a second label, out of the at least twolabels, are associated with a node in a social graph of a socialnetworking system. The co-occurrence metric associated with the at leasttwo labels can be incremented based on the number of times. It can bedetermined that the co-occurrence metric at least meets a specifiedco-occurrence threshold.

In an embodiment, the node can be associated with at least one of aparticular label, a particular content item, a particular entity, aparticular page, a particular group, a particular event, or a particularplace.

In an embodiment, the at least two labels can be determined to berelated based on the topic similarity metric. Determining that the atleast two labels are related can further comprise acquiring textualinformation associated with the set of content items. A set of topicdistributions for the set of content items can be determined based onthe textual information. The set of topic distributions can include afirst topic distribution for a first content item in the set of contentitems and a second topic distribution for a second content item in theset of content items. The first topic distribution can be associatedwith a first label out of the at least two labels and the second topicdistribution can be associated with a second label out of the at leasttwo labels. The first label can be descriptive of the first content itemand the second label can be descriptive of the second content item. Thetopic similarity metric can be calculated based on comparing the firsttopic distribution and the second topic distribution. It can bedetermined that the topic similarity metric at least meets a specifiedtopic similarity threshold.

In an embodiment, the textual information associated with the set ofcontent items can include at least one of a respective caption for eachcontent item in the set of content items or a respective description foreach content item in the set of content items.

In an embodiment, the one label can be selected based on having ahighest social engagement metric with respect to the at least twolabels.

In an embodiment, a suggestion to utilize the one label can be provided.

In an embodiment, the set of labels can include a set of hashtags.

In an embodiment, the set of hashtags can be determined to be trendingwith respect to at least one of a specified time period or a specifiedrecent hashtag amount.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example related labelmodule configured to facilitate identifying and grouping related contentlabels, according to an embodiment of the present disclosure.

FIG. 2A illustrates an example label acquisition module configured tofacilitate identifying and grouping related content labels, according toan embodiment of the present disclosure.

FIG. 2B illustrates an example relation determination module configuredto facilitate identifying and grouping related content labels, accordingto an embodiment of the present disclosure.

FIG. 3 illustrates an example scenario associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 4 illustrates an example scenario associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 5 illustrates an example method associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 6A illustrates an example method associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 6B illustrates an example method associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 6C illustrates an example method associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure.

FIG. 7 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Identifying and Grouping Related Content Labels

People use computing devices (or systems) for various purposes.Computing devices can provide different kinds of functionality. Userscan utilize their computing devices to produce content, such as bywriting text, taking pictures, or recording videos. Users can also usetheir computing devices to consume content, such as by reading articles,viewing media, or accessing web resources (e.g., web sites, onlineservices, etc.). In some cases, users of a social networking system (orservice) can use their computing devices to post or publish content viathe social networking system. In some instances, the content to beposted or published via the social networking system can be tagged orlabeled by the users, such as with content labels (i.e., labels forcontent items). For example, the content can be tagged with one or morelabels such as hashtags.

Under conventional approaches to utilizing labels such as hashtags, auser can provide hashtags (or other labels) to be associated withcontent items or posts (e.g., status updates, links, images, videos,etc.). When another user views the content items or posts, for instance,the other user can see the hashtags associated with the content items orposts. If the other user clicks on or otherwise engages with thehashtags, the other user can be provided with additional content itemsthat are also tagged or associated with those hashtags. However, for aparticular content item, a user often times provides multiple hashtagsthat are substantially similar or related to each other, such that thehashtags are undesirably repetitive or redundant. Moreover, in somecases, conventional approaches can provide an identified set of labels,such as a list of trending or popular hashtags. However, in accordancewith conventional approaches, the identified set of labels can includerepetitive, redundant, or otherwise related labels.

Due to these or other concerns, conventional approaches can beinconvenient, inefficient, or undesirable. Therefore, an improvedapproach can be beneficial for addressing or alleviating variousdrawbacks associated with conventional approaches. The disclosedtechnology can identify and group related content labels. Variousembodiments of the present disclosure can acquire a set of labelsassociated with a set of content items. Each label in the set of labelscan be associated with at least one content item in the set of contentitems. It can be determined that at least two labels, out of the set oflabels, are related. The at least two labels can be determined to berelated based on at least one of a co-occurrence metric associated withthe at least two labels or a topic similarity metric associated with theat least two labels. One label can be selected, out of the at least twolabels, as being representative of the at least two labels. It iscontemplated that there can be many variations and/or otherpossibilities.

FIG. 1 illustrates an example system 100 including an example relatedlabel module 102 configured to facilitate identifying and groupingrelated content labels, according to an embodiment of the presentdisclosure. As shown in the example of FIG. 1, the related label module102 can include a label acquisition module 104, a relation determinationmodule 106, and a label selection module 108. In some instances, theexample system 100 can include at least one data store 110. Thecomponents (e.g., modules, elements, etc.) shown in this figure and allfigures herein are exemplary only, and other implementations may includeadditional, fewer, integrated, or different components. Some componentsmay not be shown so as not to obscure relevant details.

In some embodiments, the related label module 102 can be implemented, inpart or in whole, as software, hardware, or any combination thereof. Ingeneral, a module as discussed herein can be associated with software,hardware, or any combination thereof. In some implementations, one ormore functions, tasks, and/or operations of modules can be carried outor performed by software routines, software processes, hardware, and/orany combination thereof. In some cases, the related label module 102 canbe implemented, in part or in whole, as software running on one or morecomputing devices or systems, such as on a user or client computingdevice. For example, the related label module 102 or at least a portionthereof can be implemented as or within an application (e.g., app), aprogram, or an applet, etc., running on a user computing device or aclient computing system, such as the user device 710 of FIG. 7. Inanother example, the related label module 102 or at least a portionthereof can be implemented using one or more computing devices orsystems that include one or more servers, such as network servers orcloud servers. In some instances, the related label module 102 can, inpart or in whole, be implemented within or configured to operate inconjunction with a social networking system (or service), such as thesocial networking system 730 of FIG. 7. It should be understood thatthere can be many variations or other possibilities.

The label acquisition module 104 can be configured to facilitateacquiring a set of labels associated with a set of content items. Eachlabel in the set of labels can be associated with at least one contentitem in the set of content items. For instance, the label acquisitionmodule 104 can acquire a list of trending or popular hashtags tagged tovarious posts within the social networking system. More detailsregarding the label acquisition module 104 will be provided below withreference to FIG. 2A.

The relation determination module 106 can be configured to facilitatedetermining that at least two labels, out of the set of labels, arerelated. In some cases, the at least two labels can be determined to berelated based on at least one of a co-occurrence metric associated withthe at least two labels or a topic similarity metric associated with theat least two labels. The detection module 106 will be discussed in moredetail with reference to FIG. 2B.

Moreover, the label selection module 108 can be configured to facilitateselecting one label, out of the at least two labels, as beingrepresentative of the at least two labels. In some embodiments, the onelabel can be selected based on having a highest social engagement metricwith respect to the at least two labels. The label selection module 108can determine a respective social engagement metric for each of the atleast two labels. The label selection module 108 can compare therespective social engagement metric for each of the at least two labelsto determine which of the at least two labels has the highest socialengagement metric. The label selection module 108 can then designate,recognize, or otherwise select the label having the highest socialengagement metric as the one label that is to be representative of theat least two labels. In one example, the social engagement metric for aparticular label can be determined based on at least one of a number oftimes the particular label has been posted (in the social networkingsystem), a recency property associated with the posting of theparticular label, a quantity of interactions (e.g., clicks, taps,shares, etc.) associated with the particular label, a quantity ofdistinct users who have utilized the particular label, or anycombination thereof, etc. It should be appreciated that all examplesherein are provided for illustrative purposes and that there can be manyvariations or other possibilities.

In some implementations, the label selection module 108 can be furtherconfigured to facilitate providing a suggestion to utilize the onelabel. In one example, the social networking system can detect that auser is posting a particular hashtag. The disclosed technology can havealready determined that the one label (e.g., hashtag) is suitable to berepresentative of the particular hashtag. For instance, the relationdetermination module 106 can have determined that the particular hashtagand the one label are related to one another, such that the one labelcan represent the particular hashtag. As such, the one label can besuggested, such as via a type-ahead, to the user to be utilized insteadof the particular hashtag. In another example, the one label can beprovided as a suggestion, instead of the particular hashtag, for a labelsearch or query. The disclosed technology can thus reduce undesirableredundancy or repetitiveness.

Furthermore, in some embodiments, the related label module 102 can beconfigured to communicate and/or operate with the at least one datastore 110, as shown in the example system 100. The at least one datastore 110 can be configured to store and maintain various types of data.In some implementations, the at least one data store 110 can storeinformation associated with the social networking system (e.g., thesocial networking system 730 of FIG. 7). The information associated withthe social networking system can include data about users, socialconnections, social interactions, locations, geo-fenced areas, maps,places, events, pages, groups, posts, communications, content, feeds,account settings, privacy settings, a social graph, and various othertypes of data. In some implementations, the at least one data store 110can store information associated with users, such as user identifiers,user information, profile information, user locations, user specifiedsettings, content produced or posted by users, and various other typesof user data. In some embodiments, the at least one data store 110 canstore information that is utilized by the related label module 102, suchas by storing labels, their associations, and their properties. Again,it is contemplated that there can be many variations or otherpossibilities.

FIG. 2A illustrates an example label acquisition module 202 configuredto facilitate identifying and grouping related content labels, accordingto an embodiment of the present disclosure. In some embodiments, thelabel acquisition module 104 of FIG. 1 can be implemented as the examplelabel acquisition module 202. As shown in FIG. 2A, the label acquisitionmodule 202 can include a label receiving module 204 and a labelretrieving module 206.

As discussed previously, the label acquisition module 202 can facilitateacquiring a set of labels associated with a set of content items. Insome embodiments, the label acquisition module 202 can utilize the labelreceiving module 204 to receive or otherwise acquire the set of labelsassociated with the set of content items. In some embodiments, the labelacquisition module 202 can utilize the label retrieving module 206 toretrieve, pull, fetch, or otherwise acquire the set of labels associatedwith the set of content items. Each label in the set of labels can beassociated with at least one content item in the set of content items.In some cases, the set of labels can include a set of hashtags. Forinstance, each label can correspond to a distinct hashtag and eachdistinct hashtag can be tagged to one or more content items in the setof content items, which can include status updates, images, videos,audio clips, links, and/or other posts within a social networkingsystem.

In some implementations, the social networking system can identify ordetermine a list of trending or popular hashtags, topics, concepts,and/or other labels, etc. In some cases, the hashtags, topics, concepts,and/or other labels, etc., can be determined to be trending or popularover a specified time period (e.g., over the past hour, over the pastday, over the past week, over a particular day, over a particular week,over a particular month, etc.). In some instances, the hashtags, topics,concepts, and/or other labels, etc., can be determined to be trending orpopular out of a specified amount of recent labels. Accordingly, the setof hashtags can, for example, be determined to be trending with respectto at least one of a specified time period (e.g., trending over the past48 hours) or a specified recent hashtag amount (e.g., trending out ofthe most recent 100,000 hashtags). In some cases, the social networkingsystem can then provide the list of hashtags, topics, concepts, and/orother labels, etc., to be received, retrieved, or otherwise acquired bythe label receiving module 204 and/or the label retrieving module 206.It should be understood that many variations are possible.

FIG. 2B illustrates an example relation determination module 222configured to facilitate identifying and grouping related contentlabels, according to an embodiment of the present disclosure. In someembodiments, the relation determination module 106 of FIG. 1 can beimplemented as the example relation determination module 222. As shownin FIG. 2B, the relation determination module 222 can include aco-occurrence module 224, a topic similarity module 226, and aprobability module 228.

As discussed above, the relation determination module 222 can facilitatedetermining that at least two labels, out of a set of labels, arerelated. In some cases, the at least two labels can be related when theyare the same, substantially similar, repetitive, redundant, and/orduplicative, etc. In one example, labels can be related when one labelis a misspelling of another (e.g., #February and #Febuary can berelated). In another example, labels can be related when they aregenerally synonymous (e.g., #valentinesday, #vday, and #valentines canall be related). In a further example, labels can be related when theyrefer to the same idea, event, occasion, incident, and/or subjectmatter, etc. (e.g., #giants, #worldseries, #sanfrancisco, and #paradecan all be related during a particular time frame). Again, all examplesprovided herein are for illustrative purposes and many variations arepossible.

In some embodiments, the relation determination module 222 can utilizethe co-occurrence module 224 to determine that the at least two labelsare related. In some cases, the co-occurrence module 224 can cause eachparticular content item, out of a set of content items, that isassociated with the at least two labels to be identified. For instance,each image, video, status update, or other post that is tagged with theat least two labels can be identified (over time). In other words, eachcontent item in which the at least two labels co-occur, coexist, and/orare tagged together, etc., can be identified (e.g., over a maximum timeperiod, over a specified time period, etc.). The co-occurrence module224 can then increment, for each particular content item that isassociated with the at least two labels, the co-occurrence metricassociated with the at least two labels. The co-occurrence metricassociated with the at least two labels can, for example, include aco-occurrence count indicating the number of times that the at least twolabels co-occur, coexist, and/or are tagged together, etc. Theco-occurrence module 224 can further determine that the co-occurrencemetric at least meets a specified co-occurrence threshold, such as adefined or preset minimum quantity, proportion, or ratio. As a result,the co-occurrence module 224 can determine or recognize that the atleast two labels are related.

In some instances, the co-occurrence module 224 can determine a numberof times in which a first label and a second label, out of the at leasttwo labels, are associated with a node in a social graph of a socialnetworking system. Examples of the node can include, but are not limitedto, a particular label, a particular content item, a particular entity,a particular page, a particular group, a particular event, and/or aparticular place, etc. The co-occurrence module 224 can then increment,based on the number of times, the co-occurrence metric associated withthe at least two labels. The co-occurrence module 224 can furtherdetermine that the co-occurrence metric at least meets a specifiedco-occurrence threshold, such as a defined or preset minimum quantity,proportion, or ratio. Accordingly, the co-occurrence module 224 candetermine or recognize that the at least two labels are related. Moredetails regarding co-occurrence are described in U.S. patent applicationSer. No. 12/347,473, filed Dec. 31, 2008, entitled “TRACKING SIGNIFICANTTOPICS OF DISCOURSE IN FORUMS”, which is hereby incorporated byreference herein in its entirety.

In some implementations, the relation determination module 222 canutilize the topic similarity module 226 to determine that the at leasttwo labels are related. In some cases, the topic similarity module 226can cause textual information associated with a set of content items tobe acquired. The textual information associated with the set of contentitems can, for example, include at least one of a respective caption foreach content item in the set of content items or a respectivedescription for each content item in the set of content items. The topicsimilarity module 226 can cause a set of topic distributions for the setof content items to be determined based on the textual information, suchas via a topic tagger/identification system. The set of topicdistributions can include a first topic distribution for a first contentitem in the set of content items and a second topic distribution for asecond content item in the set of content items. The topic similaritymodule 226 can also associate the first topic distribution with a firstlabel out of the at least two labels and the second topic distributionwith a second label out of the at least two labels. The first label canbe descriptive of the first content item and the second label can bedescriptive of the second content item. The topic similarity module 226can further calculate the topic similarity metric based on comparing thefirst topic distribution and the second topic distribution. Forinstance, the topic similarity metric can include a confidence scorethat indicates or estimates how similar the first topic distribution andthe second topic distribution are with respect to each other. Moreover,the topic similarity module 226 can determine that the topic similaritymetric at least meets a specified topic similarity threshold, such as adefined or preset minimum score, value, or level. As such, the topicsimilarity module 226 can determine or recognize that the at least twolabels are related.

In some implementations, various metrics utilized with the disclosedtechnology can be determined based on calculating or evaluating cosinedistances between vectors (e.g., co-occurrence vectors, topicdistribution vectors, etc.). In some cases, the vectors can be weighted.It is contemplated that many variations are possible.

Furthermore, in some embodiments, the relation determination module 222can utilize the probability module 228 to cause a set of K labels to beidentified such that the set includes K labels that are important,novel, and diverse (i.e., with reduced redundancy and reducedrepetitiveness, etc.). In some cases, a particular label in this set canbe representative of at least two labels that are determined to berelated.

In some implementations, the probability module 228 can utilize variousalgorithms and probabilities, such as a probability P^(H) of observing aparticular hashtag now (or within a specified time period) and aprobability P₀ ^(H) of observing the particular hashtag in general (orin the past). The ratio

$\frac{P^{H}}{P_{0}^{H}}$

can be associated with a novelty aspect of the particular hashtag. Theprobability module 228 can utilize the point-wise Kullback-Leibler (KL)divergence function (i.e., point-wise information gain)

${P^{H}\log_{b}\frac{P^{H}}{P_{0}^{H}}},$

which can be associated with an importance aspect and the novelty aspectof the particular hashtag. In some cases, the set of K labels caninclude a first hashtag h₁ through a K-th hashtag h_(K), where H={h₁, .. . , h_(K)}. The probability module 228 can utilize the Hunter-Worsleyprobability bound, which states thatP^(H)≤Σ_(hϵH)P^(h)−∝Σ_((i,j)ϵT)P^(h) ^(i) ^(,h) ^(j) . As such, thejoint probability P^(H) is less than or equal to the sum, over allhashtags in H, of the unigram probability for the hashtag h minus thesum over some set of bigram probabilities T, where the set T includes(i, j).

In some embodiments, the probability module 228 can select or identify aparticular label (or a particular hashtag) out of the set of labels (ora set of hashtags) that has the least contribution to the informationgain. For the particular label, the probability module 228 can attemptto identify another label that replaces the particular label and thatalso improves the information gain. In order to identify a subset oflabels that is optimal or improved with respect to importance, novelty,and diversity, the probability module 228 can plug in various labels tothe function

$P^{H}\log_{b}\frac{P^{H}}{P_{0}^{H}}$

in attempt to maximize the function. The subset of labels used tomaximize the function can correspond to an optimal or improved subset oflabels, with respect to the importance, novelty, and diversity aspects.Additionally, in some cases, the log base b can be tunable to adjust aweight for the novelty aspect and the variable ∝ can be tunable toadjust a weight for the diversity aspect. In some instances, theco-occurrence metric and/or the topic similarly metric can also beassociated with the diversity aspect. It should be understood that manyvariations are possible.

FIG. 3 illustrates an example scenario 300 associated with identifyingand grouping related content labels, according to an embodiment of thepresent disclosure. As shown, the example scenario 300 illustrates afirst interface portion 302 of a social networking system (e.g., thesocial networking system 730 of FIG. 7) and a second interface portion304.

In the example scenario 300 of FIG. 3, the first interface portion 302can provide or present a first list 306 of trending labels, such ashashtags, prior to utilizing the disclosed technology. In the first list306, many hashtags can be related (e.g., substantially similar, withinan allowable deviation of being the same, etc.). For instance, thehashtags #vday, #valentinesday, #bemine, #roses, and #chocolate can allbe related, while the hashtags #trade and #tradesurplus can also berelated. The hashtags #solarenergy and #energy can further be related.In this instance, the hashtag #football is not related to any otherhashtag in the first list 306.

The example scenario 300 further illustrates the second interfaceportion 304, which can correspond to the first interface portion 302subsequent to the disclosed technology being utilized. The secondinterface portion 304 can provide or present a second list 308 oftrending labels, such as hashtags, subsequent to utilizing the disclosedtechnology. The each group or cluster of hashtags in the first list 306that has been determined by the disclosed technology to be related canbe replaced by a respective representative hashtag. As shown in theexample scenario 300, the second list 308 can include #vday to represent#vday, #valentinesday, #bemine, #roses, and #chocolate. The second list308 can also include #trade to represent #trade and #tradesurplus.Moreover, the second list 308 can include #solarenergy to represent#solarenergy and #energy. The second list 308 can further include#football. As discussed, it should be appreciated that all examplesprovided herein are for illustrative purposes and that many variationsare possible.

FIG. 4 illustrates an example scenario 400 associated with identifyingand grouping related content labels, according to an embodiment of thepresent disclosure. The example scenario 400 illustrates an exampleinterface 402 associated with a social networking system (e.g., thesocial networking system 730 of FIG. 7). As shown in FIG. 4, the exampleinterface 402 can include a particular interface portion for providing alist 404 of trending labels or hashtags selected and provided using thedisclosed technology.

Furthermore, in the example scenario 400, the interface 402 can alsoprovide a search bar or tool 406. As shown in this example, a user hasinputted a search term, #valentines 408. The disclosed technology candetermine that the term #valentines 408 is included in a particulargroup or cluster of related hashtags represented by a particularhashtag, #vday. Accordingly, the disclosed technology can provide orpresent the representative hashtag #vday 410 as a suggestion orrecommendation for the user's search. Again, all examples herein areprovided for illustrative purposes and many variations are possible.

FIG. 5 illustrates an example method 500 associated with identifying andgrouping related content labels, according to an embodiment of thepresent disclosure. It should be appreciated that there can beadditional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments unless otherwise stated.

At block 502, the example method 500 can acquire a set of labelsassociated with a set of content items. Each label in the set of labelscan be associated with at least one content item in the set of contentitems. At block 504, the example method 500 can determine that at leasttwo labels, out of the set of labels, are related. The at least twolabels can be determined to be related based on at least one of aco-occurrence metric associated with the at least two labels or a topicsimilarity metric associated with the at least two labels. At block 506,the example method 500 can select one label, out of the at least twolabels, as being representative of the at least two labels.

FIG. 6A illustrates an example method 600 associated with identifyingand grouping related content labels, according to an embodiment of thepresent disclosure. As discussed, it should be understood that there canbe additional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments unless otherwise stated.

At block 602, the example method 600 can identify each particularcontent item, out of the set of content items, that is associated withthe at least two labels. At block 604, the example method 600 canincrement, for each particular content item that is associated with theat least two labels, the co-occurrence metric associated with the atleast two labels. At block 606, the example method 600 can determinethat the co-occurrence metric at least meets a specified co-occurrencethreshold.

FIG. 6B illustrates an example method 620 associated with identifyingand grouping related content labels, according to an embodiment of thepresent disclosure. It is contemplated that there can be additional,fewer, or alternative steps performed in similar or alternative orders,or in parallel, within the scope of the various embodiments unlessotherwise stated.

At block 622, the example method 620 can determine a number of times inwhich a first label and a second label, out of the at least two labels,are associated with a node in a social graph of a social networkingsystem. At block 624, the example method 620 can increment, based on thenumber of times, the co-occurrence metric associated with the at leasttwo labels. At block 626, the example method 620 can determine that theco-occurrence metric at least meets a specified co-occurrence threshold.

FIG. 6C illustrates an example method 640 associated with identifyingand grouping related content labels, according to an embodiment of thepresent disclosure. Again, it should be understood that there can beadditional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments unless otherwise stated.

At block 642, the example method 640 can acquire textual informationassociated with the set of content items. At block 644, the examplemethod 640 can determine, based on the textual information, a set oftopic distributions for the set of content items. The set of topicdistributions can include a first topic distribution for a first contentitem in the set of content items and a second topic distribution for asecond content item in the set of content items. At block 646, theexample method 640 can associate the first topic distribution with afirst label out of the at least two labels and the second topicdistribution with a second label out of the at least two labels. Thefirst label can be descriptive of the first content item and the secondlabel can be descriptive of the second content item. At block 648, theexample method 640 can calculate the topic similarity metric based oncomparing the first topic distribution and the second topicdistribution. At block 650, the example method 640 can determine thatthe topic similarity metric at least meets a specified topic similaritythreshold.

It is contemplated that there can be many other uses, applications,features, possibilities, and/or variations associated with the variousembodiments of the present disclosure. For example, in some instances,the disclosed technology can be applied or utilized with subject matterother than labels. Moreover, in some cases, users can choose whether ornot to opt-in to utilize the disclosed technology. The disclosedtechnology can, for instance, also ensure that various privacy settingsand preferences are maintained and can prevent private information frombeing divulged. In another example, various embodiments of the presentdisclosure can learn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 7 illustrates a network diagram of an example system 700 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 700 includes one or more user devices710, one or more external systems 720, a social networking system (orservice) 730, and a network 750. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 730. For purposes of illustration, the embodiment of the system700, shown by FIG. 7, includes a single external system 720 and a singleuser device 710. However, in other embodiments, the system 700 mayinclude more user devices 710 and/or more external systems 720. Incertain embodiments, the social networking system 730 is operated by asocial network provider, whereas the external systems 720 are separatefrom the social networking system 730 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 730 and the external systems 720 operate inconjunction to provide social networking services to users (or members)of the social networking system 730. In this sense, the socialnetworking system 730 provides a platform or backbone, which othersystems, such as external systems 720, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 710 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 750. In one embodiment, the user device 710 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 710 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 710 is configured tocommunicate via the network 750. The user device 710 can execute anapplication, for example, a browser application that allows a user ofthe user device 710 to interact with the social networking system 730.In another embodiment, the user device 710 interacts with the socialnetworking system 730 through an application programming interface (API)provided by the native operating system of the user device 710, such asiOS and ANDROID. The user device 710 is configured to communicate withthe external system 720 and the social networking system 730 via thenetwork 750, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 750 uses standard communicationstechnologies and protocols. Thus, the network 750 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network750 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 750 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 710 may display content from theexternal system 720 and/or from the social networking system 730 byprocessing a markup language document 714 received from the externalsystem 720 and from the social networking system 730 using a browserapplication 712. The markup language document 714 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 714, the browser application 712 displays the identifiedcontent using the format or presentation described by the markuplanguage document 714. For example, the markup language document 714includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 720 and the social networking system 730. In variousembodiments, the markup language document 714 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 714 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 720 andthe user device 710. The browser application 712 on the user device 710may use a JavaScript compiler to decode the markup language document714.

The markup language document 714 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 710 also includes one or more cookies716 including data indicating whether a user of the user device 710 islogged into the social networking system 730, which may enablemodification of the data communicated from the social networking system730 to the user device 710.

The external system 720 includes one or more web servers that includeone or more web pages 722 a, 722 b, which are communicated to the userdevice 710 using the network 750. The external system 720 is separatefrom the social networking system 730. For example, the external system720 is associated with a first domain, while the social networkingsystem 730 is associated with a separate social networking domain. Webpages 722 a, 722 b, included in the external system 720, comprise markuplanguage documents 714 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 730 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 730 may be administered, managed, or controlled by anoperator. The operator of the social networking system 730 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 730. Any type of operator may beused.

Users may join the social networking system 730 and then add connectionsto any number of other users of the social networking system 730 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 730 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 730. For example, in an embodiment, if users in thesocial networking system 730 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 730 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 730 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 730 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 730 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system730 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 730 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system730 provides users with the ability to take actions on various types ofitems supported by the social networking system 730. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 730 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 730, transactions that allow users to buy or sellitems via services provided by or through the social networking system730, and interactions with advertisements that a user may perform on oroff the social networking system 730. These are just a few examples ofthe items upon which a user may act on the social networking system 730,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 730 or inthe external system 720, separate from the social networking system 730,or coupled to the social networking system 730 via the network 750.

The social networking system 730 is also capable of linking a variety ofentities. For example, the social networking system 730 enables users tointeract with each other as well as external systems 720 or otherentities through an API, a web service, or other communication channels.The social networking system 730 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 730. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 730 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 730 also includes user-generated content,which enhances a user's interactions with the social networking system730. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 730. For example, a usercommunicates posts to the social networking system 730 from a userdevice 710. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 730 by a third party. Content“items” are represented as objects in the social networking system 730.In this way, users of the social networking system 730 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 730.

The social networking system 730 includes a web server 732, an APIrequest server 734, a user profile store 736, a connection store 738, anaction logger 740, an activity log 742, and an authorization server 744.In an embodiment of the invention, the social networking system 730 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 736 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 730. This information is storedin the user profile store 736 such that each user is uniquelyidentified. The social networking system 730 also stores data describingone or more connections between different users in the connection store738. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 730 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 730, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 738.

The social networking system 730 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 736and the connection store 738 store instances of the corresponding typeof objects maintained by the social networking system 730. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store736 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 730initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 730, the social networking system 730 generatesa new instance of a user profile in the user profile store 736, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 738 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 720 or connections to other entities. The connection store 738may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 736 and the connection store 738 may beimplemented as a federated database.

Data stored in the connection store 738, the user profile store 736, andthe activity log 742 enables the social networking system 730 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 730, user accounts of thefirst user and the second user from the user profile store 736 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 738 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 730. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 730 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 730). The image may itself be represented as a node in the socialnetworking system 730. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 736, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 742. By generating and maintaining thesocial graph, the social networking system 730 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 732 links the social networking system 730 to one or moreuser devices 710 and/or one or more external systems 720 via the network750. The web server 732 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 732 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system730 and one or more user devices 710. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 734 allows one or more external systems 720 anduser devices 710 to call access information from the social networkingsystem 730 by calling one or more API functions. The API request server734 may also allow external systems 720 to send information to thesocial networking system 730 by calling APIs. The external system 720,in one embodiment, sends an API request to the social networking system730 via the network 750, and the API request server 734 receives the APIrequest. The API request server 734 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 734 communicates to the external system 720via the network 750. For example, responsive to an API request, the APIrequest server 734 collects data associated with a user, such as theuser's connections that have logged into the external system 720, andcommunicates the collected data to the external system 720. In anotherembodiment, the user device 710 communicates with the social networkingsystem 730 via APIs in the same manner as external systems 720.

The action logger 740 is capable of receiving communications from theweb server 732 about user actions on and/or off the social networkingsystem 730. The action logger 740 populates the activity log 742 withinformation about user actions, enabling the social networking system730 to discover various actions taken by its users within the socialnetworking system 730 and outside of the social networking system 730.Any action that a particular user takes with respect to another node onthe social networking system 730 may be associated with each user'saccount, through information maintained in the activity log 742 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 730 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 730, the action isrecorded in the activity log 742. In one embodiment, the socialnetworking system 730 maintains the activity log 742 as a database ofentries. When an action is taken within the social networking system730, an entry for the action is added to the activity log 742. Theactivity log 742 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 730,such as an external system 720 that is separate from the socialnetworking system 730. For example, the action logger 740 may receivedata describing a user's interaction with an external system 720 fromthe web server 732. In this example, the external system 720 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system720 include a user expressing an interest in an external system 720 oranother entity, a user posting a comment to the social networking system730 that discusses an external system 720 or a web page 722 a within theexternal system 720, a user posting to the social networking system 730a Uniform Resource Locator (URL) or other identifier associated with anexternal system 720, a user attending an event associated with anexternal system 720, or any other action by a user that is related to anexternal system 720. Thus, the activity log 742 may include actionsdescribing interactions between a user of the social networking system730 and an external system 720 that is separate from the socialnetworking system 730.

The authorization server 744 enforces one or more privacy settings ofthe users of the social networking system 730. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 720, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems720. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 720 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 720 toaccess the user's work information, but specify a list of externalsystems 720 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 720 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 744 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 720, and/or other applications and entities. Theexternal system 720 may need authorization from the authorization server744 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 744 determines if another user, the external system720, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 730 can include arelated label module 746. The related label module 746 can, for example,be implemented as the related label module 102 of FIG. 1. As discussedpreviously, it should be appreciated that there can be many variationsor other possibilities. For example, in some instances, the relatedlabel module 746 (or at least a portion thereof) can be included in theuser device 710. Other features of the related label module 746 arediscussed herein in connection with the related label module 102.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 8 illustrates anexample of a computer system 800 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 800 includes sets ofinstructions for causing the computer system 800 to perform theprocesses and features discussed herein. The computer system 800 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 800 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 800 may be the social networking system 730, the user device 710,and the external system 820, or a component thereof. In an embodiment ofthe invention, the computer system 800 may be one server among many thatconstitutes all or part of the social networking system 730.

The computer system 800 includes a processor 802, a cache 804, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 800 includes a high performanceinput/output (I/O) bus 806 and a standard I/O bus 808. A host bridge 810couples processor 802 to high performance I/O bus 806, whereas I/O busbridge 812 couples the two buses 806 and 808 to each other. A systemmemory 814 and one or more network interfaces 816 couple to highperformance I/O bus 806. The computer system 800 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 818 and I/O ports 820 couple to the standard I/Obus 808. The computer system 800 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 808. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 800, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 800 are described in greater detailbelow. In particular, the network interface 816 provides communicationbetween the computer system 800 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 818 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 814 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor802. The I/O ports 820 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 800.

The computer system 800 may include a variety of system architectures,and various components of the computer system 800 may be rearranged. Forexample, the cache 804 may be on-chip with processor 802. Alternatively,the cache 804 and the processor 802 may be packed together as a“processor module”, with processor 802 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 808 may couple to thehigh performance I/O bus 806. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 800being coupled to the single bus. Moreover, the computer system 800 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 800 that, when read and executed by one or moreprocessors, cause the computer system 800 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system800, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 802.Initially, the series of instructions may be stored on a storage device,such as the mass storage 818. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 816. The instructions are copied from thestorage device, such as the mass storage 818, into the system memory 814and then accessed and executed by the processor 802. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system800 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments. Furthermore, reference in this specification to “based on”can mean “based, at least in part, on”, “based on at least aportion/part of”, “at least a portion/part of which is based on”, and/orany combination thereof.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:determining, by a computing system, that at least a first label and asecond label associated with content items of a system are related;selecting, by the computing system, the first label as representative ofthe second label based on usage of the first label and the second labelon the system; and replacing, by the computing system, the second labelwith the first label for an action taken on the system.
 2. Thecomputer-implemented method of claim 1, wherein the system is a socialnetworking system and the selecting the first label as representative ofthe second label based on usage of the first label and the second labelcomprises: determining a social engagement metric for the first label;and determining a social engagement metric for the second label.
 3. Thecomputer-implemented method of claim 2, wherein the selecting the firstlabel as representative of the second label based on usage of the firstlabel and the second label further comprises: determining the socialengagement metric for the first label is greater than the socialengagement metric for the second label; and selecting the first label asrepresentative of the second label based on the determining the socialengagement metric for the first label is greater than the socialengagement metric for the second label.
 4. The computer-implementedmethod of claim 2, wherein the social engagement metric for a label isbased on a number of times the label has been posted.
 5. Thecomputer-implemented method of claim 2, wherein the social engagementmetric for a label is based on a recency property associated withposting of the label.
 6. The computer-implemented method of claim 2,wherein the social engagement metric for a label is based on a quantityof interactions associated with the label.
 7. The computer-implementedmethod of claim 6, wherein the interactions comprise at least one ofclicks, taps, or shares.
 8. The computer-implemented method of claim 2,wherein the social engagement metric for a label is based on a quantityof distinct users who have utilized the label.
 9. Thecomputer-implemented method of claim 1, wherein at least one of thefirst label or the second label are among a set of labels that aretrending on the system.
 10. The computer-implemented method of claim 1,wherein the replacing the second label with the first label for theaction taken on the system comprises: detecting that the second label isbeing entered; and providing the first label instead of the secondlabel.
 11. A computing system comprising: at least one processor; and amemory storing instructions that, when executed by the at least oneprocessor, cause the computing system to perform: determining that aplurality of labels associated with content items of a system arerelated; selecting a first label of the plurality of labels asrepresentative of the plurality of labels based on usage of theplurality of labels on the system; and utilizing the first label insteadof other labels of the plurality of labels for user actions taken on thesystem.
 12. The computing system of claim 11, wherein the system is asocial networking system and the selecting the first label of theplurality of labels as representative of the plurality of labels basedon usage of the plurality of labels comprises: determining a socialengagement metric for the first label; and determining social engagementmetrics for the other labels.
 13. The computing system of claim 12,wherein the selecting the first label of the plurality of labels asrepresentative of the plurality of labels based on usage of theplurality of labels further comprises: determining the social engagementmetric for the first label is greater than the social engagement metricsfor the other labels; and selecting the first label as representative ofthe plurality of labels based on the determining the social engagementmetric for the first label is greater than the social engagement metricsfor the other labels.
 14. The computing system of claim 12, wherein thesocial engagement metric for a label is based on a number of times thelabel has been posted.
 15. The computing system of claim 12, wherein thesocial engagement metric for a label is based on a recency propertyassociated with posting of the label.
 16. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by at least one processor of a computing system, cause thecomputing system to perform a method comprising: determining that aplurality of hashtags associated with content items of a socialnetworking system are related; selecting a first hashtag of theplurality of hashtags as representative of the plurality of hashtagsbased on usage of the plurality of hashtags on the social networkingsystem; and utilizing the first hashtag instead of other hashtags of theplurality of hashtags for a user action taken on the social networkingsystem.
 17. The non-transitory computer-readable storage medium of claim16, wherein the selecting the first hashtag of the plurality of hashtagsas representative of the plurality of hashtags based on usage of theplurality of hashtags comprises: determining a social engagement metricfor the first hashtag; and determining social engagement metrics for theother hashtags.
 18. The non-transitory computer-readable storage mediumof claim 17, wherein the selecting the first hashtag of the plurality ofhashtags as representative of the plurality of hashtags based on usageof the plurality of hashtags further comprises: determining the socialengagement metric for the first hashtag is greater than the socialengagement metrics for the other hashtags; and selecting the firsthashtag as representative of the plurality of hashtags based on thedetermining the social engagement metric for the first hashtag isgreater than the social engagement metrics for the other hashtags. 19.The non-transitory computer-readable storage medium of claim 17, whereinthe social engagement metric for a hashtag is based on a quantity ofinteractions associated with the hashtag.
 20. The non-transitorycomputer-readable storage medium of claim 17, wherein the socialengagement metric for a hashtag is based on a quantity of distinct userswho have utilized the hashtag.